Item


SLAM With Dynamic Targets via Single-Cluster PHD Filtering

This paper presents the first algorithm for simultaneous localization and mapping (SLAM) that can estimate the locations of both dynamic and static features in addition to the vehicle trajectory. We model the feature-based SLAM problem as a single-cluster process, where the vehicle motion defines the parent, and the map features define the daughter. Based on this assumption, we obtain tractable formulae that define a Bayesian filter recursion. The novelty in this filter is that it provides a robust multi-object likelihood which is easily understood in the context of our starting assumptions. We present a particle/Gaussian mixture implementation of the filter, taking into consideration the challenges that SLAM presents over target tracking with stationary sensors, such as changing fields of view and a mixture of static and dynamic map features. Monte Carlo simulation results are given which demonstrate the filter’s effectiveness with high measurement clutter and non-linear vehicle motion

Manuscript received September 09, 2012; revised December 09, 2012; accepted February 19, 2013. Date of publication March 06, 2013; date of current version May 13, 2013. This work was supported by an EPSRC grant EP/J012432/1, EU grant FP7-ICT-2011-7 project PANDORA Ref 288273, Spanish Ministry of Science and Innovation project RAIMON ref. CTM2011-29691-C02-02) and the Catalan Government (FI and BE-DGR grants).. The work of C. S. Lee was supported by a Ph.D. FI Scholarship of the Catalan Government. The work of D. E. Clark was supported by an RAEng/EPSRC Fellowship. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ba-Ngu Vo

Institute of Electrical and Electronics Engineers (IEEE)

Manager: Ministerio de Ciencia e Innovación (Espanya)
Author: Lee, Chee Sing
Clark, Daniel E.
Salvi, Joaquim
Abstract: This paper presents the first algorithm for simultaneous localization and mapping (SLAM) that can estimate the locations of both dynamic and static features in addition to the vehicle trajectory. We model the feature-based SLAM problem as a single-cluster process, where the vehicle motion defines the parent, and the map features define the daughter. Based on this assumption, we obtain tractable formulae that define a Bayesian filter recursion. The novelty in this filter is that it provides a robust multi-object likelihood which is easily understood in the context of our starting assumptions. We present a particle/Gaussian mixture implementation of the filter, taking into consideration the challenges that SLAM presents over target tracking with stationary sensors, such as changing fields of view and a mixture of static and dynamic map features. Monte Carlo simulation results are given which demonstrate the filter’s effectiveness with high measurement clutter and non-linear vehicle motion
Manuscript received September 09, 2012; revised December 09, 2012; accepted February 19, 2013. Date of publication March 06, 2013; date of current version May 13, 2013. This work was supported by an EPSRC grant EP/J012432/1, EU grant FP7-ICT-2011-7 project PANDORA Ref 288273, Spanish Ministry of Science and Innovation project RAIMON ref. CTM2011-29691-C02-02) and the Catalan Government (FI and BE-DGR grants).. The work of C. S. Lee was supported by a Ph.D. FI Scholarship of the Catalan Government. The work of D. E. Clark was supported by an RAEng/EPSRC Fellowship. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ba-Ngu Vo
Document access: http://hdl.handle.net/2072/294962
Language: eng
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Rights: Tots els drets reservats
Subject: Algorismes computacionals
Computer algorithms
Imatges -- Processament
Image processing
Processos estocàstics
Stochastic processes
Title: SLAM With Dynamic Targets via Single-Cluster PHD Filtering
Type: info:eu-repo/semantics/article
Repository: Recercat

Subjects

Authors